Econometric Literature Research Articles

Staggered interventions with no control groups.

The limitations of the two-way fixed effects for the impact evaluation of interventions that occur at different times for each group have meant that 'staggered interventions' have been highlighted in recent years in the econometric literature and, more recently, in epidemiology. Although many alternative strategies (such as staggered difference-in-differences) have been proposed, the focus has predominantly been on scenarios in which one or more control groups are available. However, control groups are often unavailable, due to limitations in the available data or because all units eventually receive the intervention. In this context, interrupted time series (ITS) designs can constitute an appropriate alternative. The extent to which common model specifications for ITS analyses are limited in the case of staggered interventions remains an underexplored area in the methodological literature. In this work, we aim to demonstrate that standard ITS model specifications typically yield biased results for staggered interventions and we propose alternative model specifications that were inspired by recent developments in the difference-in-differences literature to propose adapted analytical strategies.

EXPLORING THE EMERGING AREAS OF CORPORATE DISCLOSURES: A BIBLIOMETRIC ANALYSIS

In the current Indian scenario of the financial reporting regime, stakeholders are increasingly looking at the disclosure practices of the companies. Therefore, higher level of transparency is required which implies that there is need of higher level of disclosure. In the present era, the sources of information are not only restricted to annual reports for stakeholders to take decisions. Hence, it is very crucial to have encyclopaedic study on corporate disclosures. The main objective of this paper is to determine the current state of research on corporate disclosure practices across the world. In the present study, literature in subject areas business, management, accounting, economics, econometrics, and finance has been retrieved from Scopus Database and analysed for 15 years between 2009 & 2023. The bibliometric analysis has been conducted using VOS Viewer. This study highlights the trend of publication across two decades, countries and journals along with most cited papers, top journals, most influential authors. Finally, through co-occurrence analysis of keywords, least explored areas have been identified. This paper highlights the gap in the available literature and make recommendations for future research in emerging areas.

Automated Machine Learning and Asset Pricing

We evaluate whether machine learning methods can better model excess portfolio returns compared to the standard regression-based strategies generally used in the finance and econometric literature. We examine 17 benchmark factor model specifications based on Expected Utility Theory and theory drawn from behavioural finance. We assess whether machine learning can identify features of the data-generating process undetected by standard methods and rank the best-performing algorithms. Our tests use 95 years of CRSP data, from 1926 to 2021, encompassing the price history of the broad US stock market. Our findings suggest that machine learning methods provide more accurate models of stock returns based on risk factors than standard regression-based methods of estimation. They also indicate that certain risk factors and combinations of risk factors may be more attractive when more appropriate account is taken of the non-linear properties of the underlying assets.

Mapping The Research Landscape of Customer Loyalty on Beauty Clinic

Beauty skin care has now become a necessity for everyone who wants healthy skin. The lifestyle of women demands more attention to appearance, for that skin care is very important for everyone. Skin and facial care is one of the main needs of modern women who will support their activities. This need is so noticed by skin health and beauty care service companies, that various skin and facial care clinic businesses have sprung up, especially in big cities. This paper contributes to this field by presenting an analysis of new econometric literature on academic research on customer loyalty in the beauty clinic industry. This method utilizes tools like VOSviewer to visualize and analyze bibliographic data, enabling researchers to identify key concepts, research directions, and emerging trends in a particular field. VOSviewer, a widely used software, helps in mapping relationships between concepts, determining research trends, and providing insights for future studies.sing. This study reviewed the comprehensive Scopus database, analyzing 383 documents to identify key metrics, such as influential publication sources, keywords, countries, affiliations, and authors. The results revealed a growing number of publications related to consumer loyalty.

Understanding of Causes of Spurious Associations: Problems and Prospects

This paper contributes to the social science literature when analyzing survey or time series data social scientists use spurious regression without due consideration of its assumptions and the data structure. This results in misinterpretation and misleading conclusions about the population. The paper reviews basic statistical and econometrics literature which led to the development of modern time series analysis in the presence of spurious regression. It concludes that the term ‘Spurious’ was well known before the Granger and Yule’s work in time series context rather than cross-sectional data. The same reasons can produce spurious regression today and surely the solution doesn’t exist in the cointegration analysis. Social scientists and applied econometrician investigators need more serious thinking and care to avoid spurious regression, if it is necessary even if data is stationary or cross-sectional. In this study, we extended the Ghouse experiment which is based on simulated data by employing real-world data to assess the effectiveness of the newly proposed Ghouse Equation in comparison to conventional approaches. The findings demonstrate that the Ghouse Equation produces the lowest probability of spurious regression as compared to its counterparts. Moreover, in forecasting performance, Ghouse Equation outperformed its counterparts. These results highlight the Ghouse Equation as a valuable and better tool for econometric analysis for nonstationary time series.

The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies

Abstract A new and rapidly growing econometric literature is making advances in the problem of using machine learning methods for causal inference questions. Yet, the empirical economics literature has not started to fully exploit the strengths of these modern methods. We revisit influential empirical studies with causal machine learning methods aiming to connect the econometric theory on these methods with empirical economics. We focus on the double machine learning, causal forest and generic machine learning methods, in the context of both average and heterogeneous treatment effects. We illustrate the implementation of these methods in a variety of settings and highlight the relevance and value added relative to traditional methods used in the original studies.

Effects of curcumin on non-alcoholic fatty liver disease: A scientific metrogy study

BackgroundNon-alcoholic fatty liver disease (NAFLD) is one of the most common chronic liver diseases encountered in clinical practice. Curcumin can alleviate insulin resistance, inhibit oxidative stress response, reduce inflammation, reduce liver fat deposition, and effectively improve NAFLD through various modalities, inhibiting the progression into cirrhosis and fibrosis. PurposeTo explore the current status, hot spots, and developing trends of curcumin in NAFLD treatment through quantitative scientific analysis to serve as a reference for subsequent studies. Study designA comprehensive analysis of the mechanism of action of curcumin in the treatment of NAFLD and methods to increase curcumin bioavailability using bibliometric analysis and literature review. MethodsThis study used VOSviewer software to analyze the literature related to curcumin treatment of NAFLD in the Web of Science (WOS) core set database. A comprehensive and in-depth review was conducted based on the results of scientific econometric research and literature review. ResultsThe review observed that curcumin can activate various signaling pathways such as AMPK and NF-κB to inhibit oxidative stress and apoptosis, thereby reflecting its pharmacological effects: lowering lipid, anti-inflammatory, reducing insulin resistance, and anti-fibrosis. These mechanisms improve or even reverse the complex pathological features of lipid metabolism disorders associated with NAFLD. Curcumin also can potentially serve as a primary regulatory target for treating hepatic steatosis using gut microbiota. However, these pharmacological effects of curcumin were limited owing to its low bioavailability. ConclusionThis review discusses NAFLD treatment with curcumin, analyzes the reasons for its low bioavailability, and introduces models for studying and methods for improving curcumin bioavailability. As research on NAFLD grows, future research should capture the trend of basic research, pay attention to clinical research, and continuously explore the therapeutic potential of curcumin.

A Bibliometric Analysis of Sharia Bonds (Sukuk) in Islamic Capital Market

Sharia bonds (Sukuk) are one of the products of the Islamic capital market; the development of Islamic bonds is rapid in countries where most of the population is Muslim, using Islamic law principles. This study systematically explores the development of Sharia bond research (Sukuk) in the Islamic capital market in the observation period for six years, namely from 2017 to 2022. Bibliometrics is a data analysis approach in this study using the VOSviewer application. Quantitative description is an analysis method used in this study to describe the econometric literature with a network visualization model on the topic to be discussed. The sample is obtained using the publish or perish application with data from Google Scholar and Scopus, so the data obtained is then sorted to become relevant sample data of 760 articles related to Sukuk in the Islamic capital market. The results of the first VOSviewer mapping are ten articles with the most citations, network visualization, overlay visualization, and density visualization, and there are five clusters between item relationships; the keyword "Sharia Bond" became a popular keyword during the observation period, so that Islamic Bonds have been in great demand as evidenced by the development of research related to Sharia bonds.

Benchmarking Travel Time and Demand Prediction Methods Using Large-scale Metro Smart Card Data

Urban mass transit systems generate large volumes of data via automated systems established for ticketing, signalling, and other operational processes. This study is motivated by the observation that despite the availability of sophisticated quantitative methods, most public transport operators are constrained in exploiting the information their datasets contain. This paper intends to address this gap in the context of real-time demand and travel time prediction with smart card data. We comparatively benchmark the predictive performance of four quantitative prediction methods: multivariate linear regression (MVLR) and semiparametric regression (SPR) widely used in the econometric literature, and random forest regression (RFR) and support vector machine regression (SVMR) from machine learning. We find that the SVMR and RFR methods are the most accurate in travel flow and travel time prediction, respectively. However, we also find that the SPR technique offers lower computation time at the expense of minor inefficiency in predictive power in comparison with the two machine learning methods.

Forecast accuracy of the linear and nonlinear autoregressive models in macroeconomic modeling

AbstractMost nonlinear vector autoregressive methods in the econometric literature are based on specific functional forms, such as the smooth transition autoregressive model. This study proposes a general form of the nonlinear vector autoregressive model based on global approximators, such as neural networks, Volterra, and Weiner series. The simulation results of 20 linear and nonlinear multivariate time series processes indicate that nonlinear vector autoregressive methods, especially multi‐output neural networks, are more accurate based on the root mean square error and model confidence set criteria. Applying the global approximator approach to a small‐scale macroeconometric model reveals that the new approach can improve forecast accuracy compared to linear and other nonlinear vector error correction models. In addition, forecasting the relevant variables in a typical exchange rate and monetary policy models based on nonlinear specifications gives more successful results than in the linear case.

State Space Estimation: from Kalman Filter Back to Least Squares

This note reviews a direct least squares estimation of a state space model and highlights its advantages over the standard Kalman filter in some applications. Although there is a close relationship between these two concepts, dual understanding of the estimation problem seems to be little appreciated by the mainstream econometric literature as well as applied researchers. Due to computational and theoretical advancements, the least squares estimation of a state space model has become a viable alternative in many fields, showing great potential in solving otherwise difficult problems. This note gathers and discusses some possible applications to illustrate the point and contribute to their wider use in practice.

What’s trending in difference-in-differences? A synthesis of the recent econometrics literature

This paper synthesizes recent advances in the econometrics of difference-in-differences (DiD) and provides concrete recommendations for practitioners. We begin by articulating a simple set of “canonical” assumptions under which the econometrics of DiD are well-understood. We then argue that recent advances in DiD methods can be broadly classified as relaxing some components of the canonical DiD setup, with a focus on (i) multiple periods and variation in treatment timing, (ii) potential violations of parallel trends, or (iii) alternative frameworks for inference. Our discussion highlights the different ways that the DiD literature has advanced beyond the canonical model, and helps to clarify when each of the papers will be relevant for empirical work. We conclude by discussing some promising areas for future research.

A Bibliometric Analysis of Profit Loss Sharing (PLS) in Islamic Banking Research Using Vosviewer Application

The aim of this investigation is to conduct a systematic examination of bibliometric data analysis utilizing VOS Viewer and to present a comprehensive, step-by-step demonstration. The analysis is intended to assist individuals who are utilizing VOSviewer for the first time in easily accessing and utilizing the software. This study uses VOSviewer's mapping tool to provide data analysis on Islamic banking profit loss sharing (PLS) and its evolution over his five years (2018-2023). The methods used were qualitatively descriptive to explain the performance of econometric literature analysis by creating network visualizations of selected topics. The search results found 996 relevant published journals from 2018 to 2023, grouped into 10 categories by year of publication. Once the journal data had been categorized, from our analysis, it was found that there were 996 articles related to profit sharing in Islamic banking that were published within the period of 2018 to 2023. Consequently, it was deduced that profit and loss sharing in Islamic banking could be linked to various other research domains, indicating that there are ample opportunities to uncover new perspectives on this subject. Additionally, this research aims to provide researchers with a source to identify promising research areas and to choose research topics.

Improving Estimates of Transitions from Satellite Data: A Hidden Markov Model Approach

Abstract Satellite-based image classification facilitates low-cost measurement of the Earth's surface composition. However, misclassified imagery can lead to misleading conclusions about transition processes. We propose a correction for transition rate estimates based on the econometric measurement error literature to extract the signal (truth) from its noisy measurement (satellite-based classifications). No ground-truth data is required in the implementation. Our proposed correction produces consistent estimates of transition rates, confirmed by longitudinal validation data, while transition rates without correction are severely biased. Using our approach, we show how eliminating deforestation in Brazil's Atlantic forest region through 2040 could save $100 billion in CO2 emissions.

Can we estimate macroforecasters’ mis-behavior?

We answer positively to this question by using Maximum Lq-Likelihood (or Deformed Likelihood) estimator. This is based on a parameter which measures the aggregate quote of judgment in the forecasting (game-based) system formed by three players—Forecaster, Policy Maker and Reality. For the first time in econometric literature, we apply this estimator to a dynamic system and derive a robust version of the Kalman Filter—the Deformed Kalman Filter (DKF). The evidence from U.S. data suggests that the judgmental dynamics exists and is correlated (but not coincident) with the phases of the Business Cycle. Furthermore its knowledge improves in-sample as well as out-of-sample estimation.

Hold-out estimates of prediction models for Markov processes

We consider the selection of prediction models for Markovian time series. For this purpose, we study the theoretical properties of the hold-out method. In the econometrics literature, the hold-out method is called ‘out-of-sample’ and is the main method to select a suitable time series model. This method consists of estimating models on a learning set and picking up the model with minimal empirical error on a validation set of future observations. Hold-out estimates are well studied in the independent case, but, as far as we know, this is not the case when the validation set is not independent of the learning set. In this paper, assuming uniform ergodicity of the Markov chain, we state generalization bounds and oracle inequalities for such method; in particular, we show that the ‘out-of-sample’ selection method is adaptive to noise condition.

Choosing tactics: Horizontal inequalities and the risk of violent and nonviolent conflict

Do disadvantaged ethnic groups favor violent over nonviolent conflict tactics? To understand when and why civil war breaks out, we need to study violent and nonviolent forms of conflict together, using analytical tools that can account for the choice between them. Yet, most large-N analyses of the causes of civil war do not account for nonviolent conflict, and vice versa. Because the mechanisms held to link horizontal inequalities to civil war closely resemble those used to explain largely nonviolent social movements, this article studies group disadvantages, political violence and nonviolent resistance together. To reduce concerns about selection bias in horizontal inequality research, it extends the analysis to socially as well as politically relevant groups around the world. A consensus is emerging that politically disadvantaged ethnic groups shun nonviolent tactics, because they lack ties to people close to the regime and its institutions. This article challenges the consensus, by showing that political group disadvantages predict nonviolent as well as violent forms of conflict. Groups’ economic status helps explain tactical choices. Among economically advantaged groups, political disadvantages increase the risk of nonviolent conflict. Among the economically disadvantaged, they facilitate violence. This pattern is strongest in situations where no policies are in place to remedy group disadvantages, in authoritarian and less economically open societies, and in analyses that account for conflicts of low intensity, to capture onsets early. The results point to the importance of economic and other forms of leverage, which have been largely overlooked in the econometric literature.

A random parameters copula-based binary logit-generalized ordered logit model with parameterized dependency: Application to active traveler injury severity analysis

A copula-based dependence approach accommodates various facets of dependence structures in building multivariate stochastic models. In existing studies, applications of copula for ordinal random variables are predominantly modeled by employing traditional ordered models (ordered logit/probit) while assuming the effects of parameters to remain the same across all observations. The methodological contributions of this study are grounded in addressing the abovementioned significant methodological gaps in the application of copula formulation by proposing a copula-based random parameters nominal-ordinal joint model construct of correlated random variables. Specifically, we propose and develop a random parameters binary logit-generalized ordered logit copula formulation while also complementing the proposed approach by accommodating the effects of unobserved heterogeneity in parameter estimates. To the best of the authors’ knowledge, this study is the first instance to incorporate generalized ordered formulation within copula in extant econometrics literature. Further, to obtain a direct effect of exogenous variables on dependence, we parameterize the copula dependence structure as a function of different covariates in six different copula structures including a wide range of dependency structures which represent radial symmetry and asymmetry, and asymptotic tail dependence. The empirical contributions of this study are grounded in the application of the proposed copula-based formulation by examining ‘active traveler (pedestrian and bicyclist) crash type’ and ‘active traveler injury severity outcomes’ as two dimensions of active travel injury severity mechanism. The model is estimated by using crash data for the years 2012 through 2018 from the state of Queensland, Australia, by employing a comprehensive set of exogenous variables. In addition, the analyses are further augmented by complementing the elasticity effects of exogenous variables.

A Sufficient Statistical Test for Dynamic Stability

In the existing Statistics and Econometrics literature, there does not exist a statistical test which may test for all kinds of roots of the characteristic polynomial leading to an unstable dynamic response, i.e., positive and negative real unit roots, complex unit roots and the roots lying inside the unit circle. This paper develops a test which is sufficient to prove dynamic stability (in the context of roots of the characteristic polynomial) of a univariate as well as a multivariate time series without having a structural break. It covers all roots (positive and negative real unit roots, complex unit roots and the roots inside the unit circle whether single or multiple) which may lead to an unstable dynamic response. Furthermore, it also indicates the number of roots causing instability in the time series. The test is much simpler in its application as compared to the existing tests as the series is strictly stationary under the null (C01, C12).

A SYSTEMATIC LITERATURE REVIEW AND BIBLIOMETRIC ANALYSIS OF MANAGEMENT PERFORMANCE

Performance control is an critical a part of human capital control and a essential constructing block of erson overall performance. Performance control is the factor of controlling overall performance inside an organization. Employee overall performance, each in phrases of effectiveness, operations, and worker productivity, is inspired through the control competencies of a company's senior control. This is supported through administrative overall performance, which influences effectiveness and efficiency. The motive of this take a look at is to describe and talk overall performance control using the PRISMA method and techniques of econometric literature analysis. Journal screening using the PRISMA method resulting 30 journals with 76 authors. Results for these variables revealed that the variables were related to employee performance and leadership.